Designing any artificial selection protocol is limited by our ability to predict the outcome of evolution, which is often challenging due to a multitude of stochastic forces at play, such as mutations, reproductive stochasticity (genetic drift) and environmental fluctuations [1]}, [2]}. In contrast to strongly divergent evolution at the genetic level, there is growing experimental evidence for convergent predictable evolution at the phenotypic level [3]}, [4]}, [5]}, including for complex molecular phenotypes like RNA polymerase function [6]}. We will exploit this evolutionary predictability and focus on designing artificial selection for molecular phenotypes, which are key links between genotypic information, organismic functions, and evolutionary fitness [1]}.
